Overview

Dataset statistics

Number of variables13
Number of observations998070
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory99.0 MiB
Average record size in memory104.0 B

Variable types

Numeric13

Warnings

ambient is highly correlated with pmHigh correlation
coolant is highly correlated with stator_yoke and 2 other fieldsHigh correlation
u_d is highly correlated with torque and 1 other fieldsHigh correlation
u_q is highly correlated with motor_speedHigh correlation
motor_speed is highly correlated with u_q and 1 other fieldsHigh correlation
torque is highly correlated with u_d and 1 other fieldsHigh correlation
i_d is highly correlated with motor_speed and 1 other fieldsHigh correlation
i_q is highly correlated with u_d and 1 other fieldsHigh correlation
pm is highly correlated with ambient and 3 other fieldsHigh correlation
stator_yoke is highly correlated with coolant and 3 other fieldsHigh correlation
stator_tooth is highly correlated with coolant and 3 other fieldsHigh correlation
stator_winding is highly correlated with coolant and 4 other fieldsHigh correlation
coolant is highly correlated with stator_yoke and 3 other fieldsHigh correlation
u_d is highly correlated with torque and 1 other fieldsHigh correlation
u_q is highly correlated with motor_speedHigh correlation
motor_speed is highly correlated with u_q and 1 other fieldsHigh correlation
torque is highly correlated with u_d and 1 other fieldsHigh correlation
i_d is highly correlated with motor_speed and 1 other fieldsHigh correlation
i_q is highly correlated with u_d and 1 other fieldsHigh correlation
pm is highly correlated with stator_yoke and 2 other fieldsHigh correlation
stator_yoke is highly correlated with coolant and 3 other fieldsHigh correlation
stator_tooth is highly correlated with coolant and 3 other fieldsHigh correlation
stator_winding is highly correlated with coolant and 4 other fieldsHigh correlation
profile_id is highly correlated with coolantHigh correlation
coolant is highly correlated with stator_yoke and 1 other fieldsHigh correlation
u_d is highly correlated with torque and 1 other fieldsHigh correlation
u_q is highly correlated with motor_speedHigh correlation
motor_speed is highly correlated with u_q and 1 other fieldsHigh correlation
torque is highly correlated with u_d and 1 other fieldsHigh correlation
i_d is highly correlated with motor_speedHigh correlation
i_q is highly correlated with u_d and 1 other fieldsHigh correlation
pm is highly correlated with stator_yoke and 2 other fieldsHigh correlation
stator_yoke is highly correlated with coolant and 3 other fieldsHigh correlation
stator_tooth is highly correlated with coolant and 3 other fieldsHigh correlation
stator_winding is highly correlated with pm and 2 other fieldsHigh correlation
torque is highly correlated with motor_speed and 4 other fieldsHigh correlation
pm is highly correlated with stator_tooth and 3 other fieldsHigh correlation
motor_speed is highly correlated with torque and 4 other fieldsHigh correlation
stator_tooth is highly correlated with pm and 3 other fieldsHigh correlation
u_d is highly correlated with torque and 5 other fieldsHigh correlation
i_q is highly correlated with torque and 4 other fieldsHigh correlation
ambient is highly correlated with profile_idHigh correlation
profile_id is highly correlated with ambient and 1 other fieldsHigh correlation
coolant is highly correlated with pm and 4 other fieldsHigh correlation
i_d is highly correlated with torque and 5 other fieldsHigh correlation
u_q is highly correlated with torque and 4 other fieldsHigh correlation
stator_winding is highly correlated with pm and 5 other fieldsHigh correlation
stator_yoke is highly correlated with pm and 3 other fieldsHigh correlation

Reproduction

Analysis started2022-03-18 13:55:45.229873
Analysis finished2022-03-18 13:57:44.336770
Duration1 minute and 59.11 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ambient
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct718720
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00390549208
Minimum-8.573954
Maximum2.9671166
Zeros0
Zeros (%)0.0%
Negative427522
Negative (%)42.8%
Memory size7.6 MiB
2022-03-18T19:27:44.436162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.573954
5-th percentile-1.93117345
Q1-0.59938534
median0.26615727
Q30.68667525
95-th percentile1.441791455
Maximum2.9671166
Range11.5410706
Interquartile range (IQR)1.28606059

Descriptive statistics

Standard deviation0.9931267822
Coefficient of variation (CV)-254.2897955
Kurtosis0.8224265656
Mean-0.00390549208
Median Absolute Deviation (MAD)0.4226622
Skewness-0.8489138249
Sum-3897.95448
Variance0.9863008055
MonotonicityNot monotonic
2022-03-18T19:27:44.529863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.688608737409
 
3.7%
-2.749857214142
 
1.4%
0.68860973061
 
0.3%
0.68860771619
 
0.2%
0.68860671054
 
0.1%
0.68860567805
 
0.1%
0.68861073736
 
0.1%
0.6886047610
 
0.1%
0.6886037564
 
0.1%
0.6886117451
 
< 0.1%
Other values (718710)937619
93.9%
ValueCountFrequency (%)
-8.5739541
< 0.1%
-7.96834141
< 0.1%
-6.1837891
< 0.1%
-5.96256541
< 0.1%
-5.77576541
< 0.1%
-5.23987251
< 0.1%
-5.1444461
< 0.1%
-5.09240631
< 0.1%
-5.06082531
< 0.1%
-4.73174571
< 0.1%
ValueCountFrequency (%)
2.96711661
< 0.1%
2.9546621
< 0.1%
2.94840261
< 0.1%
2.9227581
< 0.1%
2.90649031
< 0.1%
2.86297921
< 0.1%
2.84799721
< 0.1%
2.82447431
< 0.1%
2.7876181
< 0.1%
2.77202581
< 0.1%

coolant
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct829538
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004722510195
Minimum-1.4293493
Maximum2.6490324
Zeros0
Zeros (%)0.0%
Negative558170
Negative (%)55.9%
Memory size7.6 MiB
2022-03-18T19:27:44.653805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.4293493
5-th percentile-1.0817008
Q1-1.0379249
median-0.17718683
Q30.650708915
95-th percentile1.953216
Maximum2.6490324
Range4.0783817
Interquartile range (IQR)1.688633815

Descriptive statistics

Standard deviation1.002423384
Coefficient of variation (CV)212.2649487
Kurtosis-0.7599463837
Mean0.004722510195
Median Absolute Deviation (MAD)0.86044477
Skewness0.6282471402
Sum4713.39575
Variance1.004852642
MonotonicityNot monotonic
2022-03-18T19:27:44.750016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.747555410515
 
1.1%
0.60958848115
 
0.8%
0.83895987871
 
0.8%
-0.300785367816
 
0.8%
-1.2111592064
 
0.2%
-0.52837871717
 
0.2%
1.747555225
 
< 0.1%
0.60958856201
 
< 0.1%
1.5199621200
 
< 0.1%
1.7475547159
 
< 0.1%
Other values (829528)959187
96.1%
ValueCountFrequency (%)
-1.42934931
< 0.1%
-1.36779991
< 0.1%
-1.32664241
< 0.1%
-1.30578261
< 0.1%
-1.29626921
< 0.1%
-1.29390651
< 0.1%
-1.28866661
< 0.1%
-1.27895981
< 0.1%
-1.27045021
< 0.1%
-1.26695471
< 0.1%
ValueCountFrequency (%)
2.64903241
< 0.1%
2.45165251
< 0.1%
2.3934921
< 0.1%
2.29743241
< 0.1%
2.29722071
< 0.1%
2.29708721
< 0.1%
2.29688621
< 0.1%
2.29684471
< 0.1%
2.29684071
< 0.1%
2.29681161
< 0.1%

u_d
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct960969
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004780418286
Minimum-1.6553733
Maximum2.2747343
Zeros0
Zeros (%)0.0%
Negative427389
Negative (%)42.8%
Memory size7.6 MiB
2022-03-18T19:27:44.848503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.6553733
5-th percentile-1.59439144
Q1-0.826358675
median0.267542165
Q30.358491015
95-th percentile1.93107685
Maximum2.2747343
Range3.9301076
Interquartile range (IQR)1.18484969

Descriptive statistics

Standard deviation0.9978781813
Coefficient of variation (CV)208.7428592
Kurtosis-0.5228583948
Mean0.004780418286
Median Absolute Deviation (MAD)0.67272144
Skewness0.1946440345
Sum4771.192079
Variance0.9957608646
MonotonicityNot monotonic
2022-03-18T19:27:44.947246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3101333421
 
< 0.1%
0.3101334618
 
< 0.1%
0.3101333716
 
< 0.1%
0.310133413
 
< 0.1%
0.310133311
 
< 0.1%
0.3101334310
 
< 0.1%
0.310133510
 
< 0.1%
0.310133288
 
< 0.1%
0.310133257
 
< 0.1%
0.310133526
 
< 0.1%
Other values (960959)997950
> 99.9%
ValueCountFrequency (%)
-1.65537331
< 0.1%
-1.65487751
< 0.1%
-1.65409551
< 0.1%
-1.65403581
< 0.1%
-1.6540021
< 0.1%
-1.65396491
< 0.1%
-1.65395411
< 0.1%
-1.65392371
< 0.1%
-1.65388051
< 0.1%
-1.65387951
< 0.1%
ValueCountFrequency (%)
2.27473431
< 0.1%
2.27437381
< 0.1%
2.2738461
< 0.1%
2.27380781
< 0.1%
2.2736671
< 0.1%
2.27363231
< 0.1%
2.273611
< 0.1%
2.2735991
< 0.1%
2.27359371
< 0.1%
2.27355581
< 0.1%

u_q
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct931072
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.005689722523
Minimum-1.8614632
Maximum1.7934983
Zeros0
Zeros (%)0.0%
Negative527167
Negative (%)52.8%
Memory size7.6 MiB
2022-03-18T19:27:45.049479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.8614632
5-th percentile-1.31498573
Q1-0.9273898
median-0.0998178875
Q30.8526253625
95-th percentile1.645368145
Maximum1.7934983
Range3.6549615
Interquartile range (IQR)1.780015163

Descriptive statistics

Standard deviation1.002330192
Coefficient of variation (CV)-176.1650393
Kurtosis-1.271315718
Mean-0.005689722523
Median Absolute Deviation (MAD)0.903017025
Skewness0.199885084
Sum-5678.741358
Variance1.004665814
MonotonicityNot monotonic
2022-03-18T19:27:45.147786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.277628141
 
< 0.1%
-1.277628234
 
< 0.1%
-1.27762825
 
< 0.1%
-1.277628318
 
< 0.1%
-1.16923114
 
< 0.1%
-1.169230913
 
< 0.1%
-1.169201413
 
< 0.1%
-1.169205412
 
< 0.1%
-1.169218312
 
< 0.1%
-1.169231412
 
< 0.1%
Other values (931062)997876
> 99.9%
ValueCountFrequency (%)
-1.86146321
< 0.1%
-1.86017471
< 0.1%
-1.85850791
< 0.1%
-1.85315621
< 0.1%
-1.85285331
< 0.1%
-1.85280871
< 0.1%
-1.85258921
< 0.1%
-1.85228731
< 0.1%
-1.85211711
< 0.1%
-1.852041
< 0.1%
ValueCountFrequency (%)
1.79349831
< 0.1%
1.79336621
< 0.1%
1.79311341
< 0.1%
1.79286781
< 0.1%
1.78968881
< 0.1%
1.78962891
< 0.1%
1.78877261
< 0.1%
1.78865891
< 0.1%
1.78792371
< 0.1%
1.78701071
< 0.1%

motor_speed
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct490798
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.006335507988
Minimum-1.3715293
Maximum2.0241637
Zeros0
Zeros (%)0.0%
Negative544499
Negative (%)54.6%
Memory size7.6 MiB
2022-03-18T19:27:45.259042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.3715293
5-th percentile-1.2224314
Q1-0.95189166
median-0.14024577
Q30.853583735
95-th percentile1.690631915
Maximum2.0241637
Range3.395693
Interquartile range (IQR)1.805475395

Descriptive statistics

Standard deviation1.001229294
Coefficient of variation (CV)-158.0345722
Kurtosis-1.166937
Mean-0.006335507988
Median Absolute Deviation (MAD)0.9004768
Skewness0.3333048473
Sum-6323.280457
Variance1.0024601
MonotonicityNot monotonic
2022-03-18T19:27:45.344705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.22243024836
 
0.5%
-1.22243054788
 
0.5%
-1.22243014723
 
0.5%
-1.222434689
 
0.5%
-1.22243034680
 
0.5%
-1.22243064645
 
0.5%
-1.22243074644
 
0.5%
-1.22243084643
 
0.5%
-1.22242994540
 
0.5%
-1.22242984450
 
0.4%
Other values (490788)951432
95.3%
ValueCountFrequency (%)
-1.37152931
< 0.1%
-1.36927031
< 0.1%
-1.35730041
< 0.1%
-1.35374741
< 0.1%
-1.32296821
< 0.1%
-1.32159041
< 0.1%
-1.27296571
< 0.1%
-1.27065621
< 0.1%
-1.23902021
< 0.1%
-1.22258621
< 0.1%
ValueCountFrequency (%)
2.02416371
< 0.1%
2.0241621
< 0.1%
2.02415061
< 0.1%
2.02414971
< 0.1%
2.02414921
< 0.1%
2.02414421
< 0.1%
2.02414161
< 0.1%
2.02413941
< 0.1%
2.02413771
< 0.1%
2.0241361
< 0.1%

torque
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct695029
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.003332850434
Minimum-3.3459527
Maximum3.0169706
Zeros0
Zeros (%)0.0%
Negative560538
Negative (%)56.2%
Memory size7.6 MiB
2022-03-18T19:27:45.454204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.3459527
5-th percentile-1.8487519
Q1-0.26691731
median-0.187246405
Q30.5471705375
95-th percentile1.69807524
Maximum3.0169706
Range6.3629233
Interquartile range (IQR)0.8140878475

Descriptive statistics

Standard deviation0.9979065644
Coefficient of variation (CV)-299.4153456
Kurtosis0.7793178685
Mean-0.003332850434
Median Absolute Deviation (MAD)0.54108959
Skewness-0.04266048839
Sum-3326.418032
Variance0.9958175113
MonotonicityNot monotonic
2022-03-18T19:27:45.544567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.25563973183291
 
18.4%
0.371283416150
 
1.6%
0.05782183310864
 
1.1%
1.31166826834
 
0.7%
1.62512976428
 
0.6%
0.998206564495
 
0.5%
0.684744953651
 
0.4%
-0.82302612229
 
0.2%
1.93859122004
 
0.2%
2.25205281701
 
0.2%
Other values (695019)760423
76.2%
ValueCountFrequency (%)
-3.34595271
 
< 0.1%
-3.34575721
 
< 0.1%
-3.34118255
< 0.1%
-3.33910581
 
< 0.1%
-3.3389181
 
< 0.1%
-3.33813451
 
< 0.1%
-3.33662411
 
< 0.1%
-3.33358555
< 0.1%
-3.33333445
< 0.1%
-3.33309221
 
< 0.1%
ValueCountFrequency (%)
3.01697061
< 0.1%
3.01689481
< 0.1%
3.0168841
< 0.1%
3.01686051
< 0.1%
3.0167771
< 0.1%
3.01661251
< 0.1%
3.01659821
< 0.1%
3.01655631
< 0.1%
3.01649381
< 0.1%
3.0164791
< 0.1%

i_d
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct661242
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006042970962
Minimum-3.2458737
Maximum1.0609372
Zeros0
Zeros (%)0.0%
Negative450127
Negative (%)45.1%
Memory size7.6 MiB
2022-03-18T19:27:45.642077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.2458737
5-th percentile-1.909265755
Q1-0.756296165
median0.21393506
Q31.013975
95-th percentile1.0291572
Maximum1.0609372
Range4.3068109
Interquartile range (IQR)1.770271165

Descriptive statistics

Standard deviation0.9989943207
Coefficient of variation (CV)165.3150953
Kurtosis-0.754099942
Mean0.006042970962
Median Absolute Deviation (MAD)0.81519444
Skewness-0.6225779728
Sum6031.308028
Variance0.9979896528
MonotonicityNot monotonic
2022-03-18T19:27:45.744458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.02914211040
 
0.1%
1.02914131027
 
0.1%
1.02914021011
 
0.1%
1.02914051008
 
0.1%
1.0291443996
 
0.1%
1.0291406993
 
0.1%
1.0291436991
 
0.1%
1.029141989
 
0.1%
1.0291404985
 
0.1%
1.0291433984
 
0.1%
Other values (661232)988046
99.0%
ValueCountFrequency (%)
-3.24587371
< 0.1%
-3.24447231
< 0.1%
-3.24221321
< 0.1%
-3.2421731
< 0.1%
-3.24111751
< 0.1%
-3.24042771
< 0.1%
-3.240271
< 0.1%
-3.2402161
< 0.1%
-3.23853371
< 0.1%
-3.23755151
< 0.1%
ValueCountFrequency (%)
1.06093721
< 0.1%
1.06071791
< 0.1%
1.06054661
< 0.1%
1.06043171
< 0.1%
1.06031531
< 0.1%
1.06026321
< 0.1%
1.06025961
< 0.1%
1.06025531
< 0.1%
1.06024011
< 0.1%
1.06023341
< 0.1%

i_q
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct655589
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.003194015566
Minimum-3.3416388
Maximum2.9141846
Zeros0
Zeros (%)0.0%
Negative552092
Negative (%)55.3%
Memory size7.6 MiB
2022-03-18T19:27:46.044711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.3416388
5-th percentile-1.82705293
Q1-0.25726893
median-0.19007564
Q30.499260005
95-th percentile1.723256
Maximum2.9141846
Range6.2558234
Interquartile range (IQR)0.756528935

Descriptive statistics

Standard deviation0.9979121472
Coefficient of variation (CV)-312.4318359
Kurtosis0.7849738434
Mean-0.003194015566
Median Absolute Deviation (MAD)0.532982265
Skewness-0.07570538428
Sum-3187.851116
Variance0.9958286535
MonotonicityNot monotonic
2022-03-18T19:27:46.130725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.48691997161
 
< 0.1%
-0.2457172153
 
< 0.1%
0.4869202146
 
< 0.1%
-0.24571535142
 
< 0.1%
-0.24571493141
 
< 0.1%
-0.24571498141
 
< 0.1%
-0.24571285141
 
< 0.1%
0.48691988140
 
< 0.1%
-0.24571685139
 
< 0.1%
-0.24571235139
 
< 0.1%
Other values (655579)996627
99.9%
ValueCountFrequency (%)
-3.34163881
< 0.1%
-3.341461
< 0.1%
-3.33680221
< 0.1%
-3.33454561
< 0.1%
-3.3327241
< 0.1%
-3.33073971
< 0.1%
-3.33072881
< 0.1%
-3.33072041
< 0.1%
-3.33071541
< 0.1%
-3.3307081
< 0.1%
ValueCountFrequency (%)
2.91418461
 
< 0.1%
2.91418151
 
< 0.1%
2.91418082
 
< 0.1%
2.91418051
 
< 0.1%
2.914181
 
< 0.1%
2.91417981
 
< 0.1%
2.9141795
< 0.1%
2.91417844
< 0.1%
2.91417814
< 0.1%
2.9141784
< 0.1%

pm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct945166
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004395794658
Minimum-2.6319911
Maximum2.9174562
Zeros0
Zeros (%)0.0%
Negative455539
Negative (%)45.6%
Memory size7.6 MiB
2022-03-18T19:27:46.228084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.6319911
5-th percentile-1.83855952
Q1-0.6723075475
median0.09436716
Q30.6806914075
95-th percentile1.55048845
Maximum2.9174562
Range5.5494473
Interquartile range (IQR)1.352998955

Descriptive statistics

Standard deviation0.9956862488
Coefficient of variation (CV)-226.5088172
Kurtosis-0.3492131725
Mean-0.004395794658
Median Absolute Deviation (MAD)0.661474095
Skewness-0.2329033951
Sum-4387.310775
Variance0.991391106
MonotonicityNot monotonic
2022-03-18T19:27:46.328968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.45012630
 
< 0.1%
1.44988979
 
< 0.1%
1.45148726
 
< 0.1%
0.443284275
 
< 0.1%
0.244279825
 
< 0.1%
0.735900165
 
< 0.1%
0.141592825
 
< 0.1%
0.14809694
 
< 0.1%
0.081886534
 
< 0.1%
0.394896364
 
< 0.1%
Other values (945156)997993
> 99.9%
ValueCountFrequency (%)
-2.63199111
< 0.1%
-2.6319831
< 0.1%
-2.63178521
< 0.1%
-2.63171651
< 0.1%
-2.6316651
< 0.1%
-2.63158151
< 0.1%
-2.63154051
< 0.1%
-2.63153031
< 0.1%
-2.63145331
< 0.1%
-2.63143851
< 0.1%
ValueCountFrequency (%)
2.91745621
< 0.1%
2.9172521
< 0.1%
2.91696861
< 0.1%
2.9167751
< 0.1%
2.9159341
< 0.1%
2.9158991
< 0.1%
2.91490821
< 0.1%
2.9140251
< 0.1%
2.91396361
< 0.1%
2.91248731
< 0.1%

stator_yoke
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861836
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0006091402995
Minimum-1.8346876
Maximum2.4491582
Zeros0
Zeros (%)0.0%
Negative529754
Negative (%)53.1%
Memory size7.6 MiB
2022-03-18T19:27:46.427043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.8346876
5-th percentile-1.55232954
Q1-0.7472654
median-0.057225688
Q30.6973442
95-th percentile1.72780516
Maximum2.4491582
Range4.2838458
Interquartile range (IQR)1.4446096

Descriptive statistics

Standard deviation1.001049157
Coefficient of variation (CV)1643.380282
Kurtosis-0.7289625926
Mean0.0006091402995
Median Absolute Deviation (MAD)0.709471
Skewness0.2572973687
Sum607.9646587
Variance1.002099414
MonotonicityNot monotonic
2022-03-18T19:27:46.525113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3975354712779
 
1.3%
-0.7347250612006
 
1.2%
0.95122267448
 
0.7%
-1.74629377069
 
0.7%
1.29104662696
 
0.3%
0.614033341177
 
0.1%
-0.057711642882
 
0.1%
2.299981577
 
0.1%
-0.39753565537
 
0.1%
0.95122296455
 
< 0.1%
Other values (861826)952444
95.4%
ValueCountFrequency (%)
-1.83468761
< 0.1%
-1.83415131
< 0.1%
-1.83355211
< 0.1%
-1.83301171
< 0.1%
-1.83299861
< 0.1%
-1.83282381
< 0.1%
-1.83274381
< 0.1%
-1.83261511
< 0.1%
-1.83255281
< 0.1%
-1.83243421
< 0.1%
ValueCountFrequency (%)
2.44915821
< 0.1%
2.44745371
< 0.1%
2.4468041
< 0.1%
2.44334721
< 0.1%
2.441511
< 0.1%
2.44077231
< 0.1%
2.43972061
< 0.1%
2.4396191
< 0.1%
2.43928121
< 0.1%
2.43900681
< 0.1%

stator_tooth
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct854787
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00220773772
Minimum-2.0661428
Maximum2.3266683
Zeros0
Zeros (%)0.0%
Negative496810
Negative (%)49.8%
Memory size7.6 MiB
2022-03-18T19:27:46.622952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0661428
5-th percentile-1.695881695
Q1-0.7619508
median0.0050849725
Q30.77223925
95-th percentile1.608755745
Maximum2.3266683
Range4.3928111
Interquartile range (IQR)1.53419005

Descriptive statistics

Standard deviation0.9995974868
Coefficient of variation (CV)-452.7700359
Kurtosis-0.779986981
Mean-0.00220773772
Median Absolute Deviation (MAD)0.7670357725
Skewness-0.06153324109
Sum-2203.476786
Variance0.9991951356
MonotonicityNot monotonic
2022-03-18T19:27:46.718809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4618584510954
 
1.1%
-1.06790329478
 
0.9%
-0.76195088642
 
0.9%
1.99162046465
 
0.6%
-1.98576025990
 
0.6%
0.77020125401
 
0.5%
-0.453608193340
 
0.3%
0.155906422450
 
0.2%
1.68566812097
 
0.2%
-1.67980791879
 
0.2%
Other values (854777)941374
94.3%
ValueCountFrequency (%)
-2.06614281
< 0.1%
-2.06485871
< 0.1%
-2.06483171
< 0.1%
-2.0640731
< 0.1%
-2.06400231
< 0.1%
-2.06394821
< 0.1%
-2.0634221
< 0.1%
-2.06322311
< 0.1%
-2.06319981
< 0.1%
-2.06316571
< 0.1%
ValueCountFrequency (%)
2.32666831
< 0.1%
2.3266611
< 0.1%
2.32664161
< 0.1%
2.32635571
< 0.1%
2.32607361
< 0.1%
2.32597641
< 0.1%
2.32569961
< 0.1%
2.3254051
< 0.1%
2.32494541
< 0.1%
2.32493141
< 0.1%

stator_winding
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct899142
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.003934760021
Minimum-2.0199726
Maximum2.653781
Zeros0
Zeros (%)0.0%
Negative496119
Negative (%)49.7%
Memory size7.6 MiB
2022-03-18T19:27:46.825688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0199726
5-th percentile-1.723065985
Q1-0.72562176
median0.0065364335
Q30.72566038
95-th percentile1.63822703
Maximum2.653781
Range4.6737536
Interquartile range (IQR)1.45128214

Descriptive statistics

Standard deviation0.9983427229
Coefficient of variation (CV)-253.7239165
Kurtosis-0.7284159975
Mean-0.003934760021
Median Absolute Deviation (MAD)0.7285693835
Skewness-0.02805511319
Sum-3927.165934
Variance0.9966881924
MonotonicityNot monotonic
2022-03-18T19:27:46.922228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.26160615548
 
1.6%
-0.976923354943
 
0.5%
0.0179784282972
 
0.3%
0.26864721405
 
0.1%
-1.22564891341
 
0.1%
-1.97182521190
 
0.1%
1.2616057640
 
0.1%
0.017978719539
 
0.1%
1.2616054261
 
< 0.1%
1.2616063224
 
< 0.1%
Other values (899132)969007
97.1%
ValueCountFrequency (%)
-2.01997261
< 0.1%
-2.01993181
< 0.1%
-2.0199151
< 0.1%
-2.01942011
< 0.1%
-2.01934891
< 0.1%
-2.01927611
< 0.1%
-2.01923781
< 0.1%
-2.01914121
< 0.1%
-2.0189661
< 0.1%
-2.01880931
< 0.1%
ValueCountFrequency (%)
2.6537811
< 0.1%
2.65180951
< 0.1%
2.6509981
< 0.1%
2.64710931
< 0.1%
2.64677171
< 0.1%
2.64624791
< 0.1%
2.64488861
< 0.1%
2.64414331
< 0.1%
2.6414251
< 0.1%
2.6413041
< 0.1%

profile_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.73200076
Minimum4
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2022-03-18T19:27:47.019125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q132
median56
Q368
95-th percentile79
Maximum81
Range77
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.07312543
Coefficient of variation (CV)0.4350927443
Kurtosis-0.652661436
Mean50.73200076
Median Absolute Deviation (MAD)15
Skewness-0.6284123886
Sum50634088
Variance487.2228664
MonotonicityNot monotonic
2022-03-18T19:27:47.101344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2043970
 
4.4%
640387
 
4.0%
6540093
 
4.0%
6636475
 
3.7%
2735360
 
3.5%
433423
 
3.3%
5833381
 
3.3%
5633122
 
3.3%
5332441
 
3.3%
7931153
 
3.1%
Other values (42)638265
63.9%
ValueCountFrequency (%)
433423
3.3%
640387
4.0%
1015255
 
1.5%
117886
 
0.8%
2043970
4.4%
2735360
3.5%
2921357
2.1%
3023862
2.4%
3115586
 
1.6%
3220959
2.1%
ValueCountFrequency (%)
8117671
1.8%
8023823
2.4%
7931153
3.1%
788444
 
0.8%
7714621
1.5%
7622187
2.2%
7513471
1.3%
7423760
2.4%
7316785
1.7%
7215300
1.5%

Interactions

2022-03-18T19:26:43.292242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:43.935503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:44.584995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:44.949815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:45.309453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:45.648966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:45.970121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:46.320061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:46.643959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:46.968961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:47.350261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:47.669111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:47.976319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:48.288053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:48.619350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:48.948771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:49.279202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:49.611894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:49.942642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:50.260300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:50.571633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:50.929327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:51.249029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:51.571722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:51.889991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:52.282408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:52.601108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:52.928046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:53.265941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:53.589501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:53.909389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:54.241902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:54.579058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:54.886377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:55.248915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:55.584521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:55.909788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:56.248619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:56.589405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:56.948138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:57.297647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:57.631589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:57.959622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:58.298121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:58.634728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:58.966400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:59.389363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:26:59.710192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:00.095550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:00.493480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:01.076567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:01.539242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:01.970986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:02.390114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:02.789175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:03.319666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:03.662599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:04.022683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:04.389395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:04.728809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:05.079048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:05.440511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:05.782401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:06.130841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:06.465901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:06.819493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:07.162493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:07.525163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:07.875401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:08.221817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:08.561275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:08.885960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:09.232079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:09.576969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:09.899176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:10.218637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:10.538648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:10.848056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:11.163468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:11.489192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:11.806863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:12.138525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:12.433709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:12.759332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:13.089237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:13.400045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:13.701859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:14.019089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:14.343375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:14.654727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:14.960524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:15.300474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:15.619853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:16.070236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:16.401588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:16.737192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:17.053129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:17.413509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:17.729033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:18.051906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:18.381213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:18.708718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:19.029930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:19.361895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:19.679059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:20.009149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:20.406811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:20.838634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:21.227508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:21.658635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:21.973216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:22.309106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:22.731846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:23.133828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:23.510922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:23.828697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:24.129311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:24.449159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:24.759420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:25.076606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:25.398506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:25.719083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:26.040375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:26.367996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:26.664189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:26.979060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:27.304090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:27.604766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:27.925238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:28.267421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:28.572717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:28.886669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:29.223645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:29.555829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:29.885260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:30.200120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:30.527575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:30.842130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:31.153582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:31.482935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:31.789048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:32.238612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:32.638379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:32.979145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:33.291721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:33.622126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:33.969022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:34.288672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:34.606723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:34.949203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:35.241187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:35.570189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:35.910311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:36.226876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:36.529519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:36.810647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:37.134836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:37.478693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:37.804262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:38.132373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:38.461529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:38.801660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:39.140689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:39.455836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:39.784844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:40.115621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:40.448595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:40.765462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-18T19:27:41.065183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-18T19:27:47.199957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-18T19:27:47.346611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-18T19:27:47.511345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-18T19:27:47.672004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-18T19:27:41.259237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-18T19:27:41.889347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ambientcoolantu_du_qmotor_speedtorquei_di_qpmstator_yokestator_toothstator_windingprofile_id
0-0.752143-1.1184460.327935-1.297858-1.222428-0.2501821.029572-0.245860-2.522071-1.831422-2.066143-2.0180334
1-0.771263-1.1170210.329665-1.297686-1.222429-0.2491331.029509-0.245832-2.522418-1.830969-2.064859-2.0176314
2-0.782892-1.1166810.332771-1.301822-1.222428-0.2494311.029448-0.245818-2.522673-1.830400-2.064073-2.0173434
3-0.780935-1.1167640.333700-1.301852-1.222430-0.2486361.032845-0.246955-2.521639-1.830333-2.063137-2.0176324
4-0.774043-1.1167750.335206-1.303118-1.222429-0.2487011.031807-0.246610-2.521900-1.830498-2.062795-2.0181454
5-0.762936-1.1169550.334901-1.303017-1.222429-0.2481971.031031-0.246341-2.522203-1.831931-2.062549-2.0178844
6-0.749228-1.1161700.335014-1.302082-1.222430-0.2479141.030493-0.246162-2.522538-1.833012-2.062115-2.0172434
7-0.738450-1.1139860.336256-1.305155-1.222432-0.2483211.030107-0.246035-2.522844-1.832182-2.061953-2.0172134
8-0.730910-1.1118280.334905-1.303790-1.222431-0.2477851.029851-0.245981-2.522808-1.831576-2.062443-2.0177394
9-0.727130-1.1094860.335988-1.305633-1.222431-0.2482941.029636-0.245888-2.522677-1.831438-2.062317-2.0181804

Last rows

ambientcoolantu_du_qmotor_speedtorquei_di_qpmstator_yokestator_toothstator_windingprofile_id
998060-0.0507810.6153960.331615-1.249884-1.222435-0.255641.029141-0.2457160.4314721.0788790.8433610.50338172
998061-0.0503190.5377940.331493-1.248210-1.222433-0.255641.029144-0.2457390.4304661.0744540.8422270.50135672
998062-0.0550870.4723260.331570-1.251317-1.222430-0.255641.029134-0.2457290.4301921.0689930.8414140.49910672
998063-0.0560770.4221670.331787-1.246931-1.222431-0.255641.029193-0.2457080.4301511.0600520.8408320.49737272
998064-0.0491280.3751270.331692-1.250184-1.222434-0.255641.029146-0.2457120.4294231.0349270.8383810.49596872
998065-0.0474970.3416380.331475-1.246114-1.222428-0.255641.029142-0.2457230.4298531.0185680.8360840.49472572
998066-0.0488390.3200220.331701-1.250655-1.222437-0.255641.029148-0.2457360.4297511.0134160.8344380.49427972
998067-0.0423500.3074150.330946-1.246852-1.222430-0.255641.029191-0.2457010.4294391.0029060.8339360.49266672
998068-0.0394330.3020820.330987-1.249505-1.222432-0.255641.029147-0.2457270.4295580.9991570.8305040.49058172
998069-0.0438030.3126660.330830-1.246590-1.222431-0.255641.029141-0.2457220.4291660.9871630.8280460.48938272